Molecular property prediction is a fundamental challenge for the chemical sciences, drug discovery and one of the oldest tasks in cheminformatics. Recent developments push the field from traditional QSAR models 3 to more expressive deep learning methodologies. Simultaneously, standard approaches, like tree-based methods with molecular fingerprints, are competitive and represent strong baselines, 4,5 especially in low-data domains common in real-world scenarios. Therefore, the most effective method for a new task often depends on the specific circumstances, requiring the evaluation of different approaches. For practitioners, this means building models regularly and manually for different datasets, tasks, and target properties.