When compared to OWA on the diverse OE62 dataset for HOMO energy prediction (Table ), attentionbased pooling can match and even outperform it depending on the attention configuration (i.e. number of attention heads and hidden dimensions). Under the same conditions, we observe a more than 20% improvement for ABP on LUMO energy prediction (Table ). This is an interesting conclusion as OWA requires the pre-computation of orbital coefficients and their explicit incorporation in the loss function.