We introduced DBX2, a novel GNN framework that enables to learn computational ensembles of small molecule-protein conformations as single graphs to predict binding modes and affinities. The model relies solely on simple energetic features derived from docking, without incorporating ligand and protein structural information that render conventional GNNs prone to memorization and consequently, poor generalization. We comprehensively evaluated DBX2 across various metrics for docking and VS tasks, underscoring its effectiveness as a robust tool for binding affinity prediction and virtual screening compared to conventional scoring functions and ML models based on single poses. At the same time, some caveats associated with the ensemble-based method emerged, especially reflected in the poor correlation between graph-level predicted and experimental binding affinities. We reasoned that these constraints can be ascribed to the limitations of the data generating process, i.e., docking, both in sampling the free energy landscape of binding and in quantitatively estimate binding energy contributions.