Reciprocal Rank Fusion (RRF) is widely used to combine sparse and dense retrievers in hybrid information retrieval, but a critical limitation is that it assigns a fixed, uniform weight to each retriever regardless of the query. Query-aware methods treat the per-query optimal weight as a single point, an assumption whose empirical validity has not been examined. We present an empirical study of eight dynamic fusion weight prediction strategies across four datasets and four sparse--dense retriever pairs. When we compare these approaches to static fusion and a per-query oracle that bounds achievable performance, several findings emerge. First, the per-query optimal fusion weight is typically an interval rather than a point. Queries can have multiple optimal intervals, and these can take on a variety of shapes. Second, 74 of 128 dynamic fusion methods tested were significantly better than standard RRF after false discovery rate correction. Third, dynamic fusion methods exhibit differing quality and latency characteristics that should be considered when choosing an appropriate fusion method. To address this last finding, we present a tiered decision framework that maps practitioner constraints, dataset properties, label availability, hardware, and latency budget to an appropriate method class. We release the code for our experiments at https://github.com/thomsonreuters/query-based-rrf.

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