The active learning process is 'seeded' by the starting data that are available on a given target of interest (and their structural diversity), with usually little or no control over what molecules are available for training. Here were tested the effect of the structural diversity of the starting set on subsequent active learning cycles. We compared starting sets with different degrees of structural diversity: low, moderate, and the innate diversity of the screening libraries. We found that, in general, the starting molecular diversity (its 'nature') has little influence on the diversity of the molecules acquired later (Fig. ) or hit retrieval (Supp. Fig. ) -owed to active-learning selections ('nurture'). The initial structural bias is quickly compensated for in the first 1-5 cycles for most methods (corresponding to 64-320 acquired molecules, Fig. ) and converges to the levels of innate diversity of the screening library (Table ). Only for similarity-based acquisition, by definition, the structural bias of the starting set lingers. In a few cases (mainly for exploitative and mutual information-based acquisition functions) fewer hits seem to be found when starting with highly similar molecules, although this proved to not be statistically significant in most cases (Supp. Fig. ). This behaviour is observed regardless of the chosen deep learning approach and datasets. These results indicate that the structural diversity of the starting set does not play a big role in hit identification when screening for several cycles, hence showing the usefulness of iterative traintest-update cycles in traversing chemical space effectively.