We implemented regression functionality by taking the arithmetic mean of the k-nearest neighbours weighted by the inverse of their normalised compression distance NCD to the query (Equation ). For all regression tasks, we also choose k = 5. As for the classification tasks, we evaluated the SMILES-encoding against DeepSMILES and SELFIES and again chose SMILES over the two alternatives for benchmarking (Table . We further evaluated the effects of augmentation for regression tasks on the two data sets ESOL and BACE (regression). Interestingly, and unlike our evaluation of augmentation on classification tasks, augmentation on regression tasks has a general, and in some cases significant, positive effect on performance ): Augmenting each SMILES in the ESOL data set with an additional 19 SMILES, that represent the same molecule but differ in atom-order, would decrease the RMSE measured for MolZip by 28% from 1.510 to 1.097. As with the classification, we choose the 11-fold augmentation and report it in Table as MolZip Aug.