In this work, we investigate the use of an attention-based pooling function on atomistic systems for the prediction of general and localised quantum properties with state-of-the-art 3D-coordinate aware models (the high-level workflow is illustrated in Figure ). The chosen design satisfies a collection of desirable features, some of which were previously mentioned, namely (i) permutation invariance with respect to node (atom) order, (ii) increased representational power compared to standard pooling operators thanks to the underlying neural networks, (iii) the ability to model arbitrary, potentially long-range or localised relationships due to the attention mechanism, and (iv) generality and simplicity; the proposed method is applicable to any molecular property (including quantum properties), and can be used as drop-in replacement on any architecture that uses traditional pooling methods without any modifications to the model itself.