As mentioned above, deep learning has proven effective when using much more data than the 20K activity data points available in PDBbind or CrossDocked. Thus, we hypothesized that an expanded dataset with orders of magnitude more binding data would result in more accurate models for predicting binders to novel proteins. Herein, we describe the development of BigBind, a dataset that maps molecular activities from ChEMBL to proteins in CrossDocked. The current version of BigBind contains 582,957 activities comprising 399,090 unique compounds and 1,107 protein pockets. Additionally, we created rigorous data splits based on pocket similarity so we can test model generalization to new pockets. We emphasize that, since this dataset does not contain explicit knowledge of ligand binding poses, models must use separate ligand and receptor graphs rather than an interaction graph. They may, however, utilize interaction graphs resulting from docked poses.