20K protein-ligand complexes, a relatively small amount of data compared to well-known and successful cases of using deep learning in domains such as image recognition and natural language processing. Because of its relatively small size, the accuracy of models trained on the PDBbind dataset has been limited. While many papers report promising results, these results are often inflated due to data leakage. This problem arises when using data splits that have the same protein in the train and test sets. The high apparent performance of such models is misleading because of the inherent similarity of complexes in the train and test sets. It has been observed that even a ligand-only K-nearest-neighbors (KNN) regressor can perform well on the PDBbind refined set, whereas models perform much worse when using clustered splits. Recently Francoeur et al. introduced the CrossDocked dataset, which ameliorates several issues with PDBbind. CrossDocked clusters experimental protein-ligand complexes into conserved pockets, following Pocketome. It includes complexes with no known activity data and augments the dataset with poses of ligands cross-docked to other receptors with the conserved binding site. It also clusters the pockets according to 3D structural similarity and uses these clusters for the data splits. The substantial expansion of the dataset with these docked poses improved the performance of neural networks that select realistic docked poses. Activity data, however, is still restricted to ligands with a known binding pose.