By providing fast and accurate predictions of molecular properties, chemical machine learning (ML) has the potential to significantly increase the speed and scope of molecular discovery. In this context, much attention has been paid on properties that are directly available from single-point electronic structure (e.g. density functional theory, DFT) calculations, such as atomization energies or molecular orbital energies. For established benchmark sets of small molecules like QM9, state-of-the-art ML models now reach extremely high accuracies for such properties, often surpassing the intrinsic error of the reference electronic structure methods.