The OFF-ON (organic fragments from organocatalysts that are non-modular) database is developed to train machine learning models aimed at predicting the behavior of structurally and conformationally diverse functional organic molecules, particularly photoswitchable organocatalysts. This dataset contains 7,869 equilibrium geometries and 67,457 non-equilibrium geometries of organic compounds and dimers, providing a broad foundation for exploring flexible molecular structures and their free energy surfaces.