Score-at-a-Time (SaaT) retrieval, utilising impact-ordered indexes, remains a relatively overlooked search strategy with increasing importance. Within the context of learned sparse representations and approximate retrieval, SaaT has proven to be a competitive alternative to the popular Document-at-a-Time (DaaT) approach. Yet, there are only two notable implementations of SaaT search engines: JASSv2 and IOQP. We are interested in the differences between these systems and how those differences affect performance. We identify differences in postings lists due to indexing, ranking functions, and quantization. Thus, we introduce ciffTools for quantizing the ciff indexes used by both search engines; eliminating these differences. Then, in a reproducibility study we reproduce previous experiments investigating the efficiency gap between the systems. They also differ in their compression codecs, with IOQP using SIMD BP-128 and JASSv2 using Elias Gamma SIMD VB. We, unexpectedly, find in another reproducibility experiment that SIMD BP-128 and QMX outperform Elias Gamma SIMD VB in situ. Finally, we investigate the CPU effect, and find the engines are affected differently. Overall, JASSv2 has faster median latency on a server-grade CPU, while on a desktop-grade they are evenly matched. IOQP has faster tail latency regardless of CPU. Our work reduces the throughput difference between JASSv2 and IOQP and offers insights into which aspects affect the efficiency of SaaT.

Impact-ordered indexes, and thus Score-at-a-Time (SaaT) retrieval, is seeing a resurgence due to the emergence of learned sparse representations [4] (LSR) which employ the use of neural networks to learn the term-document weights. Within this context, SaaT has been found to be a competitive alternative to the more studied Document-at-a-Time (DaaT) approach.