Although there have been several pioneering active learning studies in the molecular domain , numerous technical and practical aspects remain unaddressed. First, the hypothesised advantage in screening efficiency that active learning offers over traditional methods has not been quantified across different scenarios, and existing studies lack a focus on hit enrichment rather than performance variation. Secondly, existing studies do not address low-data settings that are commonplace in drug discovery, where little training data is leveraged to screen compound libraries many orders of magnitude larger (Supp. Table ). Finally, how molecular diversity in the initial starting set affects the success of an active learning campaign has not been addressed. Even though active learning is expected to gain relevance in the future, e.g., in the context of automated molecule discovery and self-driving labs , the current lack of computational guidelines on how to use it in practice might hamper its adoption. This first-in-time systematic study on active deep learning in low-data scenarios aims to illuminate such known unknowns and lay the foundations for its application in realistic drug screening scenarios. We focus on several facets of the active learning pipeline, such as hit discovery of different deep learning architectures and acquisition functions, the effect of starting set size and molecular diversity, and the relative improvements of active learning compared to non-iterative approaches in low-and very-low data regimes. The proposed frame of reference comprises of recommendations and observations for the prospective use of active deep learning in low-data drug discovery, and it is expected to accelerate its adoption.