By combining three pillars -MOF design based on chemical insights, property evaluation with a DFT workflow, and machine-learning predictions -, we developed an efficient strategy to advance the exploration of the MOF design space for photocatalytic applications. We could obtain a reasonably accurate machine-learning model that can be used to predict the photocatalytic potential of a MOF using a relatively small training set. This is a nice illustration of the power of the MOFTransformer model, in which general knowledge is leveraged by fine-tuning relatively small datasets. However, for this to work optimally, one does need a balanced dataset. We used chemical insights and intuition to create our CDP-MOF database to obtain such a balanced dataset.