Several studies suggest that structure-based vHTS CNN-based methods minimally rely on features of the receptor but instead distinguish active from inactive compounds primarily by ligand-based features. Carefully designed data-augmentation can draw in receptor-based features in these models, thereby promoting generalizability. By contrast, receptor features in graph-based models can affect binding mode prediction . We designed AtomNet's configurable convolutional layers to deconvolve the role of the receptor and the ligand in determining pose quality, and to probe and optimize the role of ligand:receptor interactions. A hyperparameter controls the number and configurations of AtomNet's message passing layers. If 'l' denotes the set of ligand atoms, 'r' denotes the set of receptor atoms, and 'lr' denotes the combined set of atoms, each layer can pass messages from 'l' to 'r', 'r' to 'l', 'l' to 'l', 'r' to 'r', and each of 'l' or 'r' in these can be replaced by 'lr'. To test the role of the ligand:receptor interface in classifying poses, we evaluated the effect of distinct layer configurations on distinguishing correctly docked poses from incorrectly docked poses for the uniprot split PDBbind data set. We tested layer configurations featuring transitions between ligand and receptor layers to focus on interface features. Table reveals that, while the network demonstrates some capacity to identify correct poses based on ligand features alone, the test AUC (PDBbind self-docking, uniprot split) increased dramatically when we included the ligand:receptor interface in the layer configurations (test AUC > 0.9), compared to a layer configuration between ligand atoms only (test AUC = 0.67). When we probed the interface with additional convolutional layers, we observed a peak in test AUC at 0.92. Adding more layers did not result in further improvements. These findings suggest that our graph convolutional architecture is sensitive to mechanisms of molecular recognition in structure-based drug design.