CIME4R extends CIME , a system developed for exploring and comparing Explainable Artificial Intelligence (XAI) outputs from chemical AI models. In contrast to CIME, CIME4R was developed for analyzing RO datasets, supporting scientists with their decision-making, and making AI-guided RO better understandable. It visualizes large and high-dimensional parameter spaces and is able to handle iterative and uncertain data, which is essential for analyzing RO data. CIME4R was developed in close collaboration between visualization experts and chemists. Throughout the design and implementation, we continuously tested CIME4R with regard to its usability and usefulness for the intended tasks. For testing we chose a subset of the deoxyfluorination dataset with results from EDBO by Shields et al. . Finally, we confirmed the usefulness of CIME4R on two different datasets, as described in Section Results. All datasets and their creation scripts are available online . In the following subsections, we elaborate on the design choices we made to support users in executing their tasks (as defined in Section User tasks and challenges) to meet their RO goals.