Like in previous experiments (Fig. ), ECFP-based models outperform graph-based models in hit enrichment (Fig. , Supp. Fig. ), with the latter struggling particularly in extremely low-data starts (2 to 4 molecules). As expected, larger starting sets tend to be better, with the magnitude of the effects of data size reduction depending on the target, apart from some serendipity-driven anomalies. When faced with little initial data (e.g., less than 10 molecules), active learning still showed remarkable hit enrichment. In early screening cycles, similarity search (Fig. ), which would be a natural choice for virtual screening 49 in extremely-low data scenarios, performs on par with active learning. Yet, active learning progressively showed a substantial improvement over similarity search throughout subsequent cycles (Fig. ). This motivates the adoption of active learning instead of similarity search for projects that are more timeand cost-permissive and that would allow a larger number of iterations (e.g., more than five).