Hybrid search combines a traditional keyword-based method like BM25 with dense vector similarity search, and it consistently outperforms either technique alone in enterprise RAG because the two methods fail on different kinds of queries. Vector search excels at matching meaning and paraphrase, so a question asking about termination clauses can retrieve a chunk that says ending the agreement even without shared words, but it can miss exact identifiers such as part numbers, error codes, or product SKUs that a keyword match would catch instantly. BM25 handles those exact-match and rare-term cases well but misses semantically related content phrased differently. Combining both, typically through reciprocal rank fusion or a weighted score blend, produces a candidate list that captures both kinds of relevance before reranking narrows it down. Most production-grade vector databases and search engines, including Qdrant, Weaviate, and OpenSearch, now support hybrid search natively, so implementing it usually means enabling both index types and tuning the fusion weights rather than building custom infrastructure. Enterprises with technical or highly structured content, such as engineering documentation or SAP records, tend to see the largest accuracy gains from hybrid search. Nanobase AI, an NVIDIA Inception Program member, implements hybrid search as the default retrieval layer in its RAG deployments.
Running two searches is only half the work
Adding BM25 alongside vector search is the easy part; the harder and more consequential part is deciding how to combine two ranked lists that use entirely different scoring scales. BM25 scores are unbounded and depend on term frequency statistics across the corpus, while vector similarity scores are typically bounded between fixed limits by design. Averaging these two scores directly, without accounting for the scale mismatch, tends to let whichever score has a wider numeric range dominate the fused ranking regardless of actual relevance.
Fusing two ranked lists correctly matters as much as running the two searches in the first place.
Reciprocal rank fusion as the standard default
Reciprocal rank fusion sidesteps the score-scale problem entirely by ignoring the raw scores and using only each result's rank position in its own list. Each document's fused score is the sum of one divided by a constant plus its rank in each list it appears in, so a document ranked highly in either the keyword or vector search contributes strongly to the fused result, and a document appearing near the top of both lists rises further still. This requires no score normalization and no per-corpus calibration, which is why it has become the common default in production hybrid search implementations.
Reciprocal rank fusion avoids the scale-mismatch problem by working on rank position instead of raw score, which is why it needs little tuning.
Fusion methods compared
| Method | How it works | Tuning required |
|---|---|---|
| Reciprocal rank fusion | Combines by rank position, not raw score | Minimal, one constant with a stable common default |
| Weighted score sum | Normalizes both scores to a common range, then applies a weight to each | Requires tuning the weight and the normalization approach per corpus |
| Learned fusion (a small model trained to combine signals) | Trains a ranking model on labeled relevance data | Requires labeled training data and ongoing retraining |
Reciprocal rank fusion is the practical default; weighted or learned fusion is worth the added tuning effort only once a labeled evaluation set exists to justify it.
When hybrid search changes the outcome most
Hybrid search delivers its biggest improvement over vector-only search on queries containing exact identifiers, product codes, error messages, or precise terminology that a paraphrase-oriented embedding model was not trained to weight heavily. Vector-only search continues to win on queries phrased very differently from the source wording, since BM25 has no mechanism for matching meaning across different words. A corpus with a large share of both query types benefits the most from combining them rather than picking one.
Hybrid search earns its added complexity most clearly on corpora where users mix exact-term lookups with natural-language questions.
Frequently asked questions
Does hybrid search always outperform vector search alone?
In enterprise settings with mixed query types, yes, in aggregate. On a corpus and query set where nearly every question is conceptual rather than keyword-based, the improvement from adding BM25 can be small, though it rarely hurts if fusion is implemented correctly.
Is reciprocal rank fusion the only way to combine BM25 and vector results?
No, weighted score combination and learned fusion models are alternatives, but reciprocal rank fusion is the most common starting point because it requires no score normalization and behaves predictably without a labeled tuning set.
Do BM25 and vector search need the same chunking?
Generally yes, running both over the same chunk boundaries keeps the fused result set coherent, since fusing rankings over differently chunked content complicates interpreting which passage actually matched.
Does hybrid search increase retrieval latency meaningfully?
It adds the cost of a second search, typically small since BM25 is computationally lightweight compared to vector similarity search, so the added latency is usually modest relative to the accuracy gain on mixed query workloads.
How Nanobase AI helps
Nanobase AI, an accepted member of the NVIDIA Inception Program, implements hybrid search with reciprocal rank fusion as a standard part of production RAG pipelines, then validates the fusion behavior against real query logs rather than assuming default parameters transfer. See the related guide on what a reranker adds on top of hybrid search and our solutions for the full retrieval stack.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.