We construct the scoring model using the DeepSet neural network architecture, incorporating the embedding of the route and the route property values. Moreover, the model is adept at learning route scores specific to the target molecule, incorporating the molecular fingerprints of specific reactions as part of the input. We perform experiments using three types of molecular fingerprint embeddings eventually choosing the one that resembles general chemical knowledge the most. These methods range from simple addition and subtraction of the individual reactant fingerprints 5 to set operations 6 and using a Transformer-based model. Crucially, we leveraged expert chemical knowledge to carefully select the scoring components and reaction embeddings, as well as to define and validate the route quality ranking. This approach proved effective in addressing the challenges posed by data sparsity, where multiple viable solutions exist for each task, yet these solutions are seldom experimentally verified.