The MFD method then facilitates a comparative analysis among different OCC models without the need of negative data for validation. The MFD method allows a distinct classification threshold to be selected for each trained model to attain improved predictive performance which is based on the anomaly score predictions generated by various OCC models. We exemplify the MFD method here by training and evaluating OCC models for the task of predicting the synthesisability of metal-organic frameworks (MOFs).