To calculate EXT_CHEM, we employ desc2D, ChemBERTa-77M-MLM, Uni-Mol, Gin supervised contextpred, Gin supervised edgepred, Gin supervised masking, Gin supervised infomax, and Roberta-Zinc480M-102M methods to produce representations of molecules from their SMILES notation. We then compute a distance matrix of the test task to the source tasks using an architecture-agnostic approach (optimal transport dataset distance, OTDD). We finally calculate the EXT_CHEM as the average (or weighted average) distance to the k-nearest source tasks from this distance matrix.