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What hybrid search combines
Hybrid search runs two different retrieval methods over the same collection and merges their results.
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- Lexical (keyword) retrieval, usually BM25 in full-text engines, ranks documents by term evidence. It is strong at exact strings: product codes, error messages, person names, and identifiers.
- Vector (semantic) retrieval compares embeddings of the query and documents. It finds passages that mean the same thing even when the wording differs, such as “laptop won’t power on” matching a page titled “troubleshooting a device that fails to boot.”
Each method fails in a different place. Keyword search misses paraphrases; vector search can blur exact identifiers or return a semantically related page that does not contain the term the user typed. The point of hybrid search is to keep both candidate lists instead of choosing one.
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The difficulty is that the two methods produce scores on different scales. A BM25 score of 14.2 and a cosine similarity of 0.83 cannot be added directly. Hybrid search therefore needs a fusion step that decides how to combine the lists.
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How the two fusion methods work
Elastic’s hybrid search documentation describes a single ranked list that combines keyword matching with similarity search, and it recommends reciprocal rank fusion (RRF) for that purpose. OpenSearch documents both RRF and score-based normalization as options. The two approaches make different bets about what the scores mean.
Reciprocal rank fusion (RRF)
RRF ignores raw scores and uses only position. For each document, it adds up a contribution from every result list in which the document appears:
score(d) = sum over lists q of 1 / (k + rank_q(d))
A document absent from a list contributes nothing from that list. Because the contribution falls off with rank, a document near the top of both lists rises above one that is first in only one. The constant k dampens the advantage of the very top positions.
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A worked example with k = 60, chosen here for illustration rather than as a vendor default:
- Keyword list: A, B, C
- Vector list: C, A, D
- A = 1/61 + 1/62 ≈ 0.03252
- C = 1/63 + 1/61 ≈ 0.03227
- B = 1/62 ≈ 0.01613
- D = 1/63 ≈ 0.01587
The fused order is A, C, B, D. Document B was the top keyword result, yet it ranks third because it appears in only one list.
def rrf(result_lists, k=60):
scores = {}
for results in result_lists:
for rank, doc_id in enumerate(results, start=1):
scores[doc_id] = scores.get(doc_id, 0) + 1 / (k + rank)
return sorted(scores, key=scores.get, reverse=True)RRF is a practical default when score scales are incomparable, because it needs no calibration. The cost is that it discards how far apart two scores are. If the gap between a strong match and a weak one carries meaning, RRF throws that information away. Its output values are also rank-derived sums, not probabilities of relevance, so they should not be shown to users or compared against a threshold.
Score-based (normalized) fusion
Score fusion rescales each list’s scores to a common range and then combines them, usually with a weight per retriever. It preserves margins: a clearly dominant keyword hit stays clearly dominant. OpenSearch’s guidance is to start with RRF when score distributions have not been measured, and to consider score normalization when the margin between a strong match and weaker results matters for your task.
Normalization is not automatically safe. A min-max rescale is sensitive to outliers; a single unusually high score can compress every other score. Score distributions also shift when the index, the embedding model, or the query mix changes, so a weight tuned in one month may drift in the next. Score fusion is only as reliable as the monitoring behind it.
Where reranking fits
A reranker is a second stage. First-stage retrieval (keyword, vector, or fused hybrid) produces a finite candidate pool, and the reranker reads each query-document pair more closely and reorders that pool. Because it compares the query and each candidate directly, it can separate subtle relevance differences that a first-stage score cannot.
Two constraints follow from this design:
- A reranker cannot find what retrieval missed. If the right document is outside the candidate pool, reordering will not bring it in. Raising candidate depth is a retrieval change with its own cost.
- Every candidate costs computation. Deeper pools improve recall up to a point and increase reranking time for every query.
Microsoft’s Architecture Center describes a semantic ranking stage in a retrieval-augmented generation (RAG) flow for Azure AI Search. In that design, semantic ranking follows BM25 or hybrid retrieval, processes the top 50 results, and emits a reranker score from 0 to 4. Those figures belong to that Azure service. Other reranking models and APIs have different candidate limits and score ranges, and you should read their own documentation before planning around a number.
What the published evidence shows
The most specific comparison available is from OpenSearch’s documentation, accessed in 2026. Across six BEIR datasets, RRF scored on average 3.86% lower NDCG@10 than a score-based hybrid pipeline. Latency and coordinator node CPU utilization were comparable between the two.
That result describes one benchmark suite and one configuration. It does not show that score fusion wins on your corpus, and a 3.86% average gap could reverse on queries that are mostly identifiers or mostly paraphrases. Treat it as a reason to test both fusion methods, not as a ranking of the two.
Microsoft’s Architecture Center calls RRF “lightweight and adds negligible latency.” That is a qualitative description in the guide, not a measured guarantee for every implementation. The documentation consulted does not publish a universal figure for cost savings from hybrid search, nor a universal quality gain from reranking. Any claim that hybrid search is “free” or that a reranker pays for itself in a given setting has to come from your own measurements.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to test whether it is the cheapest win for your search
- Build a realistic query set. Collect real user queries, or a representative sample, and include exact names and identifiers, terminology mismatches, and natural-language questions. Add graded relevance judgments for the documents that matter to each query.
- Compare retrieval modes on the same corpus. Run lexical-only, vector-only, hybrid with RRF, and hybrid with score fusion. Hold the corpus, query set, candidate depth, and other settings constant so that any difference is attributable to the method.
- Measure reranking against no reranking on the same candidates. If the reranked run also changed the first-stage retriever, you cannot tell which change produced the gain.
- Track both quality and operating cost. Record ranking metrics such as NDCG@10, MRR, or Precision@k, along with query latency at the 95th percentile and CPU or GPU use. Choose candidate depth by balancing recall against latency; no universal depth is established.
- Price the chosen deployment yourself. Cost depends on your vendor’s current price model, query volume, candidate depth, and whether you host the model or call an API. Check the vendor’s pricing page on the day you decide, because published prices change.
Choosing among the options
| Choice | Useful when | Watch for |
|---|---|---|
| RRF fusion | Score scales are incomparable, score variance is high, or no weighting has been calibrated yet. | Ignores score margins. In the OpenSearch benchmark, RRF averaged 3.86% lower NDCG@10 than score-based hybrid across six BEIR datasets. RRF values are not calibrated relevance probabilities. |
| Normalized score fusion | Gaps between scores carry relevance information and you can validate the weighting on your own queries. | Outliers and shifting score distributions make normalization sensitive; weights need ongoing checks. |
| Add a reranker | Relevant documents are already retrieved but ranked poorly near the top. | Added latency and compute per candidate. It cannot recover missing candidates. Gains must be measured. |
| Increase candidate depth | Relevant documents are missing before reranking and deeper retrieval improves recall. | More candidates mean more reranking work per query, with no guaranteed quality improvement. |
A practical order of operations
If you have a working keyword index and a vector index, adding RRF is usually the smallest change. Measure it first. Add score fusion only if your evaluation shows that score margins matter. Add a reranker last, and only if the relevant documents already appear in your top candidates but sit too low. If they do not appear, increase candidate depth or fix retrieval before paying for a reranker.
Whether this sequence is cheap in your environment depends on numbers you have to gather yourself: your query volume, your latency budget, and your vendor’s current pricing.
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