The main objective of our work is to explore deep dreaming as a versatile tool to generate MOF populations with systematically shifted properties, all within the native architecture of the property prediction model itself. By placing special emphasis on the physical interpretability of our model and its ability to rapidly generate MOF structures, we deviate from the pursuit of a singular "best" material. Instead, this approach aims to position us within a design space that is inherently closer to the desired functionalities from the outset, which establishes a foundational platform for further exploratory studies in MOF research.