When designing high-throughput synthesis our goal was to generate synthesis routes for 2000 target molecules in less than an hour, using a maximal reaction step of three. In an earlier phase of our quest to achieve this goal, we utilized a smaller database consisting of 100K subset of the USPTO reaction data set for prototyping purposes, and we used the FG query. When we scaled up the database to include all USPTO and the subset of SAVI reactions, we witnessed an observable deterioration of the performance using the same query. As a first measure to address the issue, we sought to optimize the FG query, which effort led to the development of the SP query. Indeed, the SP query lent us an improved performance. However, the performance was still orders of magnitudes behind of the envisioned goal, on the scale of days (data not shown). Interestingly, the development of the synthesis route finding algorithm, SynGPS , drew our attention to database artifacts. That is, the presence of multiproduct and ambiguous reactions. Additionally, we also identified duplicate reactions in the knowledge graph. To address these issues, we took a data engineering approach to investigate if the performance of finding synthesis routes can be improved by handling these artifacts with minimal impact on the original data. We refer to this process as streamlining the database. Next, we needed to investigate, whether the original FG query would be more performant on a streamlined database.