Due to method scaling e ciency for memory usage, a subset of the ANI-1 data set was taken for training representations using BOB/KRR and BOB/BRR. For consistency, ECFP/RFR and BOB/RFR were additionally trained on this subset. The subset consists of 5 nonequilibrium geometries for every molecule with up to 7 heavy atoms, as well as 5 nonequilibrium geometries for half of the molecules with 8 heavy atoms, to create a training set consisting of 33,496 molecules and 167,480 non-equilibrium geometries. All molecules from the test set were removed from the training set. This training set was additionally used for BOB/RFR and ECFP/RFR. An additional subset of the first 5000 non-equilibrium geometries was used for FCHL/KRR. Increasing the training set for FCHL/KRR had a negative impact on prediction performance so our results are with the model trained on 1000 di↵erent molecules for a total of 5000 non-equilibrium geometries.