One of the central trials models face in a low-data setting is out-of-domain generalization. Since deep learning models are typically trained on a specific set of molecules (and the corresponding structure-activity relationships), they might be challenged in generalizing to new, unseen molecules that may come from a different distribution (e.g., novel molecular scaffolds, structural motifs, or binding modes). Although this aspect is relevant for deep learning in general, low-data regimes intrinsically put additional strains on the model's ability to generalize out of the (limited) training distribution. Awareness of prediction uncertainty and out-ofdistribution performance are expected to become crucial guides in the future for prospective decision making, especially in low-data scenarios. We also expect causal and explainable deep learning to become instrumental tools in low-data scenarios and for out-ofdomain generalization, by shedding light on causal relationships, spurious correlations, and potential model shortcuts.