The presented results suggest that attention-based pooling is preferable to sum-based pooling for intensive, localised properties such as HOMO and LUMO energy prediction. Perhaps less expected is the uplift in performance on other properties that are not as localised as the HOMO and LUMO energies, for example total energy prediction on QMugs or energy prediction on the small MD17 datasets. In this latter case, it is also remarkable that a data-driven method like ABP is able to often outperform standard pooling when training with around 1,000 data points. Apart from the physical motivation behind improving localised property prediction, it should be noted that the attention mechanism adds an additional layer of expressivity to the network, enabling better approximation of general-purpose properties. Moreover, although we have presented the 'main' network such as SchNet or DimeNet++ and the pooling function as separate components, they work synergistically, especially for highly-expressive learnable and differentiable pooling functions. It is not unlikely that the patterns learnt at the pooling-level can propagate to the main model and lead to an improved holistic model behaviour.