In recent years, artificial intelligence in the form of deep learning has permeated the molecular sciences. Deep learning -based on artificial neural networks with multiple processing layers -has demonstrated remarkable potential in numerous applications, such as protein structure prediction and organic reaction planning . Deep learning has three main advantages: (a) it can learn complex and highly non-linear patterns from data , (b) it can be trained on a wide range of molecular representations (e.g., SMILES strings and graphs, Fig. ), and (c) it can be adapted to various types of training regimes, facilitating the development of tailored models for diverse applications. These aspects open novel modeling avenues compared to using human-engineered features only . Yet, its transformative potential has primarily been harnessed in data-rich settings where extensive datasets are readily available (e.g., , ). This relates to the fact that deep learning approaches optimize millions (or even billions) of neural-network parameters, which is made more robust by training on large datasets. Drug discovery, on the contrary, is often a low-data endeavor.