In this work, we developed a threefold strategy that synergistically combines chemical insights, first-principles calculations, and machine learning. We used chemical insights to generate in silico MOFs that are aimed at filling gaps in the chemical design space in areas relevant to photocatalysis. These materials contributed to an increase in the diversity metrics of linker and metal node chemistry. We then calculated DFT-based photocatalytic descriptors of a balanced dataset comprising our generated MOFs, MOFs from the QMOF database, and experimental MOF photocatalysts. With this dataset, we were able to fine-tune pre-trained machine-learning methods -MOFTransformer, and GPT-J -that reduced the cost of our exploration by predicting binary outcomes of each photocatalytic descriptor on a much larger number of MOFs. We focus on MOFs for overall water splitting, often referred to as the "Holy Grail" in the energy landscape. Throughout this process, our aim was to gain insight into the structure-property relationship and identify trends in the MOF design space.