One of the most significant data limitations is the sparsity of the protein-ligand matrix, which presents a good representation of the protein space but limited chemical space. The chemical space of the ligands co-crystallized with a protein is usually diverse, which hinders the learning of the impact of small modifications in ligand structure and more importantly facilitates model overfitting on ligand chemical properties. As a result, DL models trained on PDBbind data often show poor performance when applied to external sets, especially drug discovery LO datasets, as studied here, where the data density is reversed with thousands of ligands (actives) for a limited number of proteins. Moreover, our results show how realistic measures of model performance can differ substantially versus testing with PDBbind core set, whose composition provides overoptimistic results.