We now turn our attention to one of the most challenging scenarios encountered during computationallyassisted early-stage drug discovery: improving predictions in an inductive setting with possible out-ofdistribution test samples relative to the training data. Here, we used the entire high-fidelity data discussed in the previous section for training, and used test molecules that were selected later in the drug discovery campaign (i.e. lacking low-fidelity labels) for evaluation. This set of test molecules is referred to as a 'no low-fidelity set'. It is possible to encounter multiple such sets within the same project. For brevity, we focus only on embeddings generated by models with adaptive readouts and the sum counterpart for comparison.