Even with the accelerated development of QML methods and the heterogeneity of recent approaches, a common element for most QML models is that they naturally operate at the level of atom representations, for example through message passing steps. However, many prediction targets of interest are formulated at the molecular level. e.g. total energy, dipole moment, highest occupied molecular orbital (HOMO) energy, (lowest unoccupied molecular orbital) LUMO energy, etc. Thus, an aggregation scheme must be used to combine the atom representations into a single molecule-level representation. This task is typically handled with simple fixed pooling functions like sum, average, or maximum. Despite their appealing simplicity, there are growing concerns regarding the representational power of this class of functions . In the following section, we also discuss the concurrently-developed Orbital Weighted Average (OWA), a physics-based method designed specifically for orbital properties and which also seeks to improve upon the standard pooling operators by exploiting the local and intensive character of the target property . Buterez et al. also highlighted the lacklustre performance of standard pooling functions in a variety of settings, but particularly on challenging molecular properties . As an alternative to standard pooling, the authors proposed replacing the fixed functions with learnable functions implemented as neural networks. When applied to conventional message passing architectures (GCN , GAT and GATv2 , GIN , PNA ) that operate on the molecular graph with node features extracted from the SMILES representation, neural pooling functions provided significant uplifts in performance and faster convergence times.