In this work, we utilize MLP-based surrogate models trained on the CSLVAE latent space over which NGT performs its search. The use of surrogates is to avoid expensive function evaluations during policy training with AtomNet ® endpoints. In particular, we take the compounds scored at each outer iteration of NGT, which have been evaluated against the screening targets with AtomNet ® , encode these compounds into the CSLVAE latent space, and then train an ensemble of MLP surrogates to each minimize an MSE loss of the predicted AtomNet ® scores. To encourage policy exploration in regions where the ensemble members disagree, we utilize a UCB-like approach where the endpoint used for purposes of reward construction is the mean of the ensemble member predictions plus some critical value times the standard deviation of their predictions. This encourages NGT to sample in regions of the latent space where the average predicted AtomNet ® score is high or sufficiently compensated by the disagreement between the ensemble members. We observe that, across outer iterations of NGT, the surrogate models become increasingly more accurate proxies for predicting AtomNet ® scores (Supporting Figure ).