Knowledge distillation is performed on two different CNN model architectures. Both architectures take a 3D grid of Gaussian-like atom type densities as input and predict a CNNscore, indicating the quality of the protein-ligand pose, and a binding affinity, as a pK. The smallest architecture, Default2018 (Figure ) is composed of a small number of convolutional and average pooling layers. The larger Dense architecture (Figure ) contains several max pooling layers, convolutions, and distint units called "dense blocks". Dense blocks are comprised of series of convolutions in which every subsequent layer is provided the outputs of all preceding layers. The Dense and Default2018 architectures are composed of 685,123 and 389,059 parameters, respectively.