Ψ (Tune VGAE) (x) = fT (x) (Final model is given by the fine-tuned predictor fT ) Standard GNN readouts such as the sum operator are fixed functions with no learnable parameters. In contrast, adaptive readouts are implemented as neural networks, and the overall GNN becomes a modular architecture composed of (1) the supervised VGAE layers and (2) an adaptive readout. Consequently, there are three possible ways to freeze components at this level: i) frozen graph convolutional layers and trainable readout, ii) trainable graph layers and frozen readout, and iii) trainable graph layers and trainable readout (no freezing). After a preliminary study on a representative collection of datasets, we decided to follow strategy i) due to empirically strong results and overall originality for transfer learning