Considering that finding a synthesis route to some target molecule can be thought of as path search problem, graph-database engines seemed like a natural fit to host our reaction knowledge graph and perform path searches. However, the observed performance of the synthesis route search did not meet our predefined criteria for a method that can drive autonomous synthesis planning in a high-throughput setting. While template-based and AI/ML-based predictive retrosynthetic and forward-synthetic methods do not necessarily suffer from the same performance issue, their use case is most valuable in scenarios where no preference for a given synthesis route exists in an in-house knowledgebase. Therefore, we found it imperative that we improve the performance of the evidence-based CASP arm of our computational platform. In this endeavour, we utilized query optimization, domain-driven data engineering and benchmarking approaches to achieve our goal, that is, to generate synthesis routes on the timescale enabling high-throughput synthesis. We provide the details of this investigation in this study which led to the breakthrough in evidence-based synthesis planning.