Hyperparameter optimization was modified to accommodate smaller training sets and, as models were faster to train, different network topologies were tested (Table ). When the training sets are so small, models are often unable to learn the syntax properly and thus generate more invalid structures. The model using 1,000 molecules was the most affected by this problem, with some models not even reaching 50% validity. This impacts the accuracy of the UC-JSD, because all molecules tend to have a sampling probability 𝑝 → 0. This makes the UC-JSD have low values because all molecules have very similar probability. For this reason, only models that had more than 50% valid structures were considered.