Finding structurally novel hits is often one of the objectives of virtual screening since it might increase the chances of success of hit-to-lead optimisation and provide access to unexplored regions in the chemical space. Hence, we analysed the hits acquired (in terms of physicochemical and structural features) at the end of the iterative procedure, compared to the respective starting set. Based on qualitative projections of chemical space, active deep learning resulted to be able to explore broader regions of the chemical space (Fig. ), whereas a traditional 'one-shot' (Fig. ) or similarity-based (Fig. ) approaches stayed in 'narrower' regions.