Computer-assisted synthesis design (CASD) systems have gained popularity in recent years, providing support in generating new synthetic ideas. However, their practical synthetic feasibility is most often limited, even though advancements in applied machine learning techniques have allowed for new perspectives and predictions of possible pathways and conditions. Tools such as ICSynth, ChemPlanner, MIT's ASKCOS, 5 AstraZeneca's AIZynthfinder, and the IBM and ETH Zürich collaboration have emerged, and proved useful, offering open-source or subscription-based access. While these tools significantly advance reaction prediction, their limitations lie in the size and quality of available data sources. Most of these systems tend to prioritize the most obvious disconnections first, among them less Transform Descriptors (Reactions Data Mining) ChemRxiv copy interesting functional groups interconversion. This does prove though that these systems do work and certainly give a fresh perspective on viewing results, but they also don't pertain high value for an expert chemist as of yet. For less experienced chemists, or with some adaption, in high-throughput scenarios, these systems already do have interesting impact.