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Hybrid search is an often misused and/or misunderstood term. In this section, we’re using the definition of “hybrid search” to mean using a combination of keyword-based and vector search. Because the vector search operates in a dense embedding space and keyword-based search operate in a sparse embedding space, their relevance scores cannot be directly compared. Combining results from multiple searches thus requires a reranking step.

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Reranking strategies

There are two common approaches for reranking search results from multiple sources.
  • Score-based: Calculate final relevance scores from the individual search algorithm scores. Examples: Reciprocal Rank Fusion (the default in LanceDB), mean reciprocal rank fusion, and weighted linear combination of semantic and keyword-based search scores.
  • Relevance-based: Discards the existing scores and calculates the relevance of each search result-query pair. Example: Cross Encoder models
If you call .rerank() on a hybrid query without passing a reranker, LanceDB defaults to RRFReranker() — a score-based reranker that uses Reciprocal Rank Fusion. This is the score-based path most readers encounter first; LinearCombinationReranker is an alternative score-based strategy you opt into explicitly.
By default, rerankers return _relevance_score. Pass return_score="all" when a reranker supports it, and you also need the original vector or FTS scores for debugging. Evaluation code can rely on returned rows being ordered by descending _relevance_score. Empty reranked result sets still include the _relevance_score column. The hybrid rerank(...) method also accepts a normalize argument that controls how the raw vector and FTS scores are made comparable before reranking:
  • normalize="score" (the default) — normalizes the raw vector and FTS scores directly.
  • normalize="rank" — converts each result list to ranks first, then normalizes.
This choice materially affects score-based rerankers (such as LinearCombinationReranker), so when you evaluate score-based strategies, treat normalize as a tunable hyperparameter alongside the reranker itself. For score-based evaluation, the main built-in knobs are:
  • RRFReranker(K=60): rank fusion with a positive smoothing constant.
  • MRRReranker(weight_vector=0.5, weight_fts=0.5): weighted reciprocal rank fusion where the weights must sum to 1.0.
  • LinearCombinationReranker(weight=0.7, fill=1.0): blends vector and FTS scores directly; fill controls how strongly to penalize a result missing from one side.
Even though there are many more strategies for reranking, there are no “universally best” ones that work well for all cases, because reranking quality is dataset and application specific. Evaluating whether a reranking strategy is a good fit is also a challenge. In the next section, we discuss an example evaluation of different reranking strategies on a sample dataset.

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Example evaluation

The table below shows our evaluation results from an experiment comparing multiple rerankers on ~800 hybrid search queries. This is a modified version of an evaluation script by LlamaIndex that measures hit-rate @ top-k.

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Using OpenAI text-embedding-ada-002

Vector Search baseline: 0.64

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Using OpenAI text-embedding-3-small

Vector Search baseline: 0.59

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Conclusion

The results show that the reranking methods can significantly improve the search relevance. However, the improvement we saw was not consistent across all rerankers. In reality, the choice of reranker likely depends on the dataset and the application. It’s also important to note that the reranking methods are not a replacement for the search methods they supplement. They are complementary and it’s likely that you’d have to tune them together to get the best results. The latency vs. recall tradeoff is also an important factor to consider when choosing the reranker. Hopefully this evaluation gives you a starting point for your own experiments with hybrid search in LanceDB!