Efficient and effective information retrieval (IR) systems are needed to fetch a large number of relevant documents and present them based on their relevance to the input queries. Previous work reported the use of sparse and dense retrievers. Sparse retrievers offer low latency but suffer from term mismatch issues, while dense retrievers improve performance at the cost of higher processing times. The literature proposed Fast-Forward Indexes, an interpolation-based re-ranking framework that leverages the benefits of both sparse and dense retrievers.

Although a lot of work was done in the field, most studies evaluate the performance of the proposed models only on the MS Marco dataset, neglecting other datasets. This study extends previous work by exploring how different sparse retrievers, employing no-encoder, uni-encoder, and bi-encoder architectures, perform in an interpolation-based re-ranking setting on datasets originating from various domains. Results show that bi-encoder-based retrievers outperform the other sparse retrievers in terms of recall but with a substantial increase in latency compared to simpler retrievers, which generally showed good performance. Further, when the retrievers were used in an interpolation-based re-ranking setting, they performed similarly in terms of ranking quality.