In the FART dataset, such compounds led to repeated entries with the same canonicalized SMILES for each taste associated. Indeed, of the 409 molecules in the dataset with multiple tastes associated, 152 (37%) only have "undefined" as an additional label and 165 (40%) are labeled as bittersweet. Because FART outputs a probability distribution across the taste labels, one could expect the probability to be higher for the two (and in rare cases three) original labels compared to the other taste classes. In other words, a bittersweet molecule should have higher probabilities for both bitter and sweet while ignoring the remaining classes. We find that in most cases FART strongly favors one out of the two or more labels, which is unsurprising given the model was also trained and evaluated on single labeled data points, see SI for further information.