Figure : General overview of the workflow followed in this work. First, we determine the embedding for both data (i.e., molecules and bioactivity label pairs) and metadata (i.e., proteins). In the next step, we determine the distance between all the training and test tasks and the hardness, which includes both external and internal hardness for each test task (external hardness is the average of the k-nearest distance to the training tasks). Finally, to verify our method, we calculate the correlation between the test task's hardness and improvement gain on each test task using a meta-learning method. The individual tasks are nearly balanced, meaning the number of active to inactive samples is similar for most tasks. The dataset was used without modifications.