Nondestructive 3D pathology is poised to play a transformative role in biomedical research and precision medicine in the decades to come, helping to usher pathology into a digital 3D era. Recent improvements in high-throughput volumetric microscopy, including light-sheet microscopy, have made it feasible for large pre-clinical and clinical specimens to be imaged in toto within reasonable time frames [Glaser, et al., Nature BME, 2017; Glaser, et al., Nature Communications, 2019]. However, these imaging methods depend upon the quality and reproducibility with which fluorescent labeling and optical clearing of thick tissue specimens is performed. In particular, while high-quality volumetric datasets can be acquired with pain-staking optimization and iteration of manual tissue-preparation protocols, high-throughput imaging assays demand that these methods be highly consistent and require minimal labor. We developed a protocol for automated micro-controller-based labeling and clearing of clinical specimens in order to generate volumetric imaging datasets that consistently mimic the appearance of “gold-standard” H&E histology. Archived formalin-fixed paraffin-embedded (FFPE) tissue blocks are first de-paraffinized with a combination of heat and xylene removal of paraffin wax. Next, specimens are put in an acidic, ethanol and water solution so that an aqueous nuclear and eosin labeling step can be achieved. This otherwise labor-intensive, two-day procedure is a critical step for automation since manual processing can lead to variabilities that will affect downstream labeling and clearing performance. Finally, specimens are cleared with a non-toxic clearing agent for refractive index-matching and 3D microscopy. By using automated micro-controller-based buffer exchange hardware, we demonstrate the reliability of these low-cost and convenient methods for imaging a diverse range of tissues. These methods will facilitate pre-clinical and clinical studies with large numbers of tissue specimens, such as those needed to validate the benefits of 3D pathology for clinical decision support.