Without an organized effort, the data sparsity problem is unlikely to be resolved by the evolution of experimental crystallography which is costly and usually avoids redundancy, focusing on novel protein and ligands, further increasing the sparsity problem. Although there has been no robust quantification, we speculate that the current number of 15-20K experimental structures needs to increase by orders of magnitude to rival the size of datasets used in other common ML supported drug-design tasks. It seems computational modeling will be required to reach this level of data and one possible solution is to use molecular docking of known actives, but we must avoid adding noise from incorrect docked binding poses. For some active molecules the uncertainty can be reduced if it has exceptionally high chemical similarity to the ligand in an experimental protein ligand structure. Fortunately, the lead optimization (LO) stage in drug discovery generates such datasets because it involves the synthesis of many analogue compounds with occasional examples being co-crystalized with the protein target. Therefore, we here generate high confidence ligandprotein complexes by using maximum common substructure docking (MCS) for series of compounds with high similarity to experimentally crystalized ligands. We demonstrate that the decrease of data sparsity increases model performance and generalization power. Also, we discuss the use of pose decoys to force the model to learn from the interactions thus avoiding ligand overfitting and, training one part of the network on ligand efficiency to force the model to learn from the interaction type and not by the number of interactions which in turn is correlated to molecular weight, a common bias leading to overfitting on the compound size.