The main challenge in fully realising the potential of AI-guided workflows is integrating active learning schemes, and the AI models and the suited acquisition function they are based on, into advanced selection funnels. The criteria used for each step of the funnels are either based on an expected error bound of a lower accuracy calculation or a physics-informed heuristic, e.g., a material having too large of an electronic band gap or being too dense. The end goal of the screening criteria is to reduce the overall cost and time of a study, while still exploring over the relevant parts of materials space.