While useful, the current screening criteria are a potential obstacle when combining active learning with high-throughput workflows. Because they are not necessarily derived from the data that underlies the AImodel, an overzealous screening procedure can exclude materials that would drastically improve model performance and possibly correct an initial bias. Importantly, the heuristics used to screen out materials may not directly relate to the target property, but be controlled by an unknown third process, leading to an incorrect physical interpretation. Furthermore, adding selection funnels to active learning frameworks could perpetuate the initial bias of the models as the dataset will be directed towards the existing conditions. One potential solution to this problem is through using multi-objective learning to simultaneously optimize both the screening criteria and the target property. However, a less complex solution would be preferable.