To stress-test active learning in low-data regimes, we further decreased the number of starting molecules available for training. We used differently sized random starting sets, containing as little as 2 molecules in the most extreme scenario, up to 64 molecules (Fig. , Supp. Fig. ). The best performing acquisition function (mutual information) was employed with both ECFP-based and graph-based models.