Our metrics are based on common approaches from the chemoinformatics community, such as distances from the training set and variance of an ensemble of machine learning models. We explore three different applicability metrics. We first chose the Tanimoto similarity based on Morgan Fingerprints, which represents a molecular similarity metric that is not tuned for any particular property. We also chose two applicability metrics based on our MPNN models. The first based on the molecular fingerprint MPNN model, were we compute the mean L 2 distance between the molecular fingerprint (see Fig. ) for a molecule and those of the 64 closest molecules from the training set. The second is based on the fingerprints for each atom as computed using the atomic contribution model, where we compute the dissimilarity of a molecule from one in the training set as the mean distance between the most-similar pairs of atoms: