1. Active learning can find more bioactive molecules in (extreme) low-data settings (up to a six-fold hit enrichment in our case) compared to traditional screening methods like similarity-search or 'one-shot' virtual screening. However, starting out with more data is generally better. 2. Active learning is more effective when screening for several cycles, e.g., five or more as per our experimental setup. With a restricted screening budget (e.g., in the order of dozens of molecules), a similarity-based approach might prove a better solution. 3. The biggest factor driving hit retrieval is the chosen acquisition function, that controls what molecules are selected for follow-up at each cycle. Using best predictions (exploitation) or a mutual information-based acquisition method yields the most bioactive molecules. These acquisition methods present no downside in their ability to discover diverse molecules. 4. A low structural diversity of the initial training set does not seem to affect hit discovery in later active learning cycles. We showed that a lack of diversity is rapidly 'corrected' for within the next 1-5 screening cycles. 5. A fingerprint-based deep learning approach has a better capacity to identify novel hits and is more apt to low-data settings compared to graph neural networks. Our results corroborate active learning as an effective tool for drug discovery, especially in low-data scenarios, in terms of dataset size, structural bias, and class imbalance. Since the method of acquiring molecules proved to be the main performance driver, there is ample room for improved acquisition functions. Acquisition functions that consider a more chemistry-centred approach, on top of model predictions, bear particular promise to improve molecular novelty among found hits. In this study, we use an approximate Bayesian approach for uncertainty prediction. Different methods of uncertainty quantification might also play a big role in molecule acquisition and could be object of future research .