In this study, we mimicked a realistic drug discovery scenario. All experiments followed the same setup (Fig. ), whereby active learning ) is selected from the screening library (step 0). This starting set can be sampled from the full library (innate structural diversity) or can be sampled from hierarchical subclusters of molecules with moderate or low structural diversity. b. The active deep learning cycle. From the training set, a model is trained and used to predict bioactivity on all unlabelled molecules in the screening library (step 1). Using an acquisition function, molecules are selected for follow-up (step 2), and subsequent labelling (step 3). Finally, the labelled molecules are added to the training set for the next cycle (step 4). models were used to iteratively query a screening library of 100,000 molecules with a maximum screening budget of 1,000 molecules. Each series of active learning experiments started with a small set of 64 molecules (step 0) sampled from the screening library, containing at least one bioactive molecule (see Materials and Methods). Notably, this procedure led to 80% of the starting sets containing less than three hits, which reflects a realistic drug discovery scenario. The active learning screening procedure then consisted of four iterative steps: and Supp. Fig. ). An additional set of 20,000 molecules was randomly selected to serve as an external test set for performance monitoring.