We create and analyze a number of transparent ML models, trained with selected 2D ligand and P-L surface descriptors, as well as P-L interaction fingerprints and SQM energy terms. We demonstrate that our hybrid SQM-ML model is able to perform well in a VS setting across all ligand decoy sets, but also achieve higher classification accuracy than the original SQM method and Glide SP. Finally, we interpret SQM-ML scoring in the datasets where it had weaker performance and suggest ways for future improvement.