Removing L ∆∆G does not significantly decrease the performance of the RBFE predictions (Table ), but increases the correlation and reduces the error of the absolute affinity prediction (Table ). However, removing L ∆G or L consistency drops the performance of the RBFE predictions by a considerable margin. The removal of L rotation has little effect on the performance of the network, indicating that data augmentation may be all that is required to provide the necessary rotational invariance. When we remove L ∆∆G and L consistency , the Siamese network no longer provides predictions that are correlated with the experimental affinity values, however, the errors of the predictions are only slightly increased from the baseline.