One potential solution to escape the training size and diversity bottlenecks is active learning . Active learning is based on the principle that a model can achieve greater accuracy with fewer training data if it is "allowed to choose the data from which it learns" . In drug discovery, active learning can be cast into an iterative screening approach (Fig. ), where instead of performing a single virtual screening experiment, one can test (fewer) molecules across multiple cycles . At each iteration, a selection of molecules is acquired based on model predictions and experimentally tested (e.g., for its biological activity on the intended target). The newly obtained experimental data, together with data previously collected, are then used to update the model, aiming to inform the next iteration. By improving a model over subsequent iterations, active learning bears promise to identify more bioactive molecules using less resources than standard 'one-shot' virtual screening approaches .