At the completion of a generation cycle (i.e., n step = N steps ), an acquisition criterion is used to select a molecule x t for the human expert to evaluate. In typical AL settings, molecules would be selected from a pool available before training the property predictor. In our setting, molecules are selected from a pool U r = {x m } M m=1 which corresponds to a set of high-scoring molecules stored in memory M r across all optimization steps from the completed generation cycle. The acquisition criterion depends on the property predictor f θ . In this study, we compare the performance of different acquisition criteria against a random sampling baseline: