By way of example, Figure shows the effect of the number of compounds picked per cycle on model recall and precision (F1 score) for the two machine learning models (GBM and GP). For a fixed total number of compounds selected (here, 2500), one might expect the model to improve at small sample sizes (hence, more active learning cycles), but we find that the efficiency is already well converged when picking 500 per cycle. Similarly, the choice of machine learning model has little effect, with slightly higher metrics for the GBM model, but both recall and precision comparisons are within the error bars. Note that for the current dataset, random selection would give a recall of 0.05 and F1 score of 0.03 for identification of the top 2% of compounds. Therefore, with recall of around 0.25-0.30 for most of our experiments, we see efficiency improvements with active learning of around a factor of 5x compared to random selection. For reference, the growth and scoring of this compound set in FEgrow requires around 1000 cpuhrs, which is not prohibitive, but automated acceleration at no cost is clearly worthwhile.