The MACE energy and force component errors on these datasets as well as ANI-2x errors on the COMP6 dataset are reported in Table . Firstly, MACE errors are comparable across the different datasets, which demonstrates the transferability of MACE over a wide range of structures represented in each of these datasets. The poorer performance on "GDB" datasets may be explained by the fact that the ZINC training set was constructed to specifically contain drug-like compounds, as loosely motivated by the context of drug metabolism, whereas the original "GDB" datasets were assembled to cover all structures with up to 13 heavy atoms. Larger errors on "GDB" datasets may also be explained by the fact that these compounds are most dissimilar to the compounds in the training set, as seen in Figure of the following section.