In this work, we develop a data-driven scoring model with expert augmentation that pro-vides target-specific and generalizable scores across chemical space for synthetic routes. By learning to predict the distance between reference synthetic routes from previous chemists' synthesis and artificial pathways generated from an open-source CASP program, AiZyn-thFinder, the model encapsulates a comprehensive implicit evaluation of synthesis routes.