Convolutional Siamese networks are capable of RBFE prediction (Figure ). We find that higher capacity CNN models used in the arms of the Siamese network increases the predictive performance of the model. Our multitask loss is able to boost the performance of the RBFE prediction in comparison to only calculating a loss on the RBFE (Table ). This indicates that RBFE prediction is aided by increased regularization of the CNN latent space. The latent space subtraction of the Siamese network is able to implicitly embed the reverse symmetry of the RBFE prediction. However, the reverse symmetry is learnable without the latent space subtraction when the model is trained on both orderings of ligands for RBFE prediction. We note that our convolutional Siamese network's performance is less consistent when applied to out of distribution examples (Figures 4, 5