We first compare the performance of a random forest model (as an example of a single-task method) with that of a prototypical network (as an example of a method that utilizes information from all source tasks) and demonstrate that the latter achieves better results for the majority of 157 test tasks. Subsequently, we reveal instances (i.e., test tasks) where tasks exhibit similar performance in the random forest but markedly different performance in the prototypical network. These examples underscore that some test tasks benefit more from sharing knowledge than others and stimulated experiments quantifying task hardness with our proposed method. Following these experiments, we conduct ablation studies to determine the sensitivity of EXT_CHEM and EXT_PROT to different representations used for molecules and proteins.