The prediction of physicochemical properties is a common task in machine learning for chemistry, as the elucidation of these properties through either laboratory high-throughput screening or simulation-based computation is expensive and time-consuming at best and intractable at worst . Hence, multiple approaches have been proposed to enable data-driven approximation of properties such as frontier molecular orbital energies, the molecular dipole moment, rotational constants, or ionisation potentials. During our exploratory study of our proposed architectures on the quantum chemical benchmark data sets QM7 and QM8 (Table ), we found that our simplest model (MSR1) exhibited better performance than both GIN(E) and D-MPNN on QM7 and better performance than GIN(E) on QM8. In this section, we further investigate the performance of our models on additional physico-and quantum-chemical prediction tasks using the OCELOT chromophore data set .