We illustrate this last point by considering HOMO energy prediction tasks on the QM7b dataset without pre-computing the orbital coefficients (Table ). The OWA method thus takes the more general, nonorbital specific WA form of Equation . The results indicate that attention-based pooling outperforms WA by about the same margin as WA pooling outperforms sum pooling. We also take the opportunity to highlight the similarity between the (O)WA methods and the well-known Deep Sets framework that considers sum-decomposable functions, where individual items are processed by simple neural networks such as MLPs before being summed . Although Deep Sets offers a theoretically sound construction, our work has previously suggested and exemplified that Deep Sets-style pooling does not match the performance of attention-based pooling .