The current work represents an extension to previous work which considered only 2D molecular graphs, but demonstrated the potential of attention-based pooling for predicting properties such as the HOMO energy . Here, we demonstrate consistent uplifts in performance (as measured by the Mean Absolute Error -MAE) compared to sum pooling, chosen as a representative of the established methods, on a selection of standard datasets of different sizes and simulated at different levels of theory, including QM7b (7,211 molecules) , QM8 (21,786 molecules) , QM9 (130,831 molecules) , QMugs (665K molecules) , and the CCSD and CCSD(T) datasets from MD17 (1,500 molecules for Aspirin, Benzene, Malonaldehyde, Toluene, and 2,000 for Ethanol) . Sum pooling is the default choice in many implementations (e.g. SchNet, DimeNet), usually outperforming mean and maximum pooling or matching them . We also evaluate the proposed methods against OWA on the OE62 dataset and conclude that attention-based pooling can match and outperform OWA depending on the configuration (e.g. number of attention heads). While our method introduces a large number of learnable parameters, this is normal for a standard attention implementation and does not significantly affect training times or introduce overfitting.