Geometric deep learning -which incorporates and processes symmetry information -is also getting increasing attention in the molecular sciences, especially in the context of complex, three-dimensional molecular systems . Incorporating symmetry information, such as invariance or equivariance to rototranslations into neural network architectures, bears promise to learn sophisticated molecular information, which might be especially relevant when little training data is available. However, little is currently known on the performance of geometric deep learning in low-data scenarios, and the incorporation of molecular symmetry might not necessarily lead to better performing approaches .