FaST is capable of optimizing for different novelty/diversity metrics. In this section, we compute the novelty/diversity metrics using atom-pair (AP) fingerprints . While Morgan fingerprints have successfully been applied to many molecular tasks such as drug screening, it has some failure modes . Namely, Morgan fingerprints is often not informative about the size or the shape of the molecules. These properties are better captured in AP fingerprints, as AP fingerprints account for all atom pairs, including their pairwise distances. We run the same experiment on the GSK3β+JNK3+QED+SA task described in Section 5, but change the fingerprint from Morgan to AP for the novelty/diversity metrics. The results are shown in Table with comparison to baselines. We observe that our method outperform baselines by a greater margin, especially in the novelty metric. This is not surprising because our model can explicitly optimize for any similarity metric, while the baseline methods are not novelty/diversity-aware during training. Interestingly, we find that optimizing for AP fingerprints also yields molecules that score high under Morgan fingerprints for this task (but the converse is not true).