Background and Purpose: Computational models using data from high throughput testing have promise for prioritizing and screening chemicals for testing under the U.S. Environmental Protection Agency’s Endocrine Disruptor Screening Program (EDSP). The purpose of this work was to demonstrate a data processing method for the determination of optimal minimal assay batteries from a larger comprehensive model, to provide a uniform method of evaluating the performance of future minimal assay batteries compared to the AR pathway model, and to incorporate chemical cluster analysis into this evaluation. To demonstrate this approach, combinations of assays in the AR pathway model were evaluated for performance; however, several of these assays are no longer available through the original vendor. Methods: We compared two previously published models and found that an expanded 14-assay model had a higher sensitivity for antagonists, whereas the original 11-assay model had a slightly higher sensitivity for agonists. We then investigated subsets of the original AR pathway model to optimize overall testing strategies that minimize cost while maintaining sensitivity across a broad chemical space. Results: Evaluation of the critical assays across subset models derived from the 14-assay model identified 3 critical assays for predicting antagonism and 2 critical assays for predicting agonism. A minimum of 9 assays is required for predicting both agonism and antagonism with high sensitivity (95%). However, testing workflows guided by chemical structure-based clusters can reduce the average number of assays needed per chemical by basing the assays selected for testing on the likelihood of a chemical being an AR agonist. Our results show that a multi-stage testing workflow can provide 95% sensitivity while requiring only 48% of the resources required for running all assays from the original full models. The resources can be reduced further by incorporating in silico activity predictions. Conclusions: This work illustrates a data-driven approach that incorporates both chemical clustering and simultaneous consideration of both antagonism and agonism mechanisms to more efficiently screen chemicals. It provides prioritization and screening strategies that minimize the overall number of assays needed for predicting AR activity, which will maximize the number of chemicals that can be tested and allow data-driven prioritization of chemicals for further screening under the EDSP. The views expressed in this abstract are those of the authors and do not necessarily reflect the views or policies of the U.S. EPA.