  We study hybrid search in text retrieval where lexical and semantic search
are fused together with the intuition that the two are complementary in how
they model relevance. In particular, we examine fusion by a convex combination
(CC) of lexical and semantic scores, as well as the Reciprocal Rank Fusion
(RRF) method, and identify their advantages and potential pitfalls. Contrary to
existing studies, we find RRF to be sensitive to its parameters; that the
learning of a CC fusion is generally agnostic to the choice of score
normalization; that CC outperforms RRF in in-domain and out-of-domain settings;
and finally, that CC is sample efficient, requiring only a small set of
training examples to tune its only parameter to a target domain.
