High-content imaging of larval zebrafish behavior can be used as a screening approach to rapidly evaluate the relative potential for chemicals to cause toxicity. However, most statistical methods applied to these data transform movement values to incidence-based "hits" and calculate lowest effect levels (LELs), which loses individual fish resolution of behavior and defies hazard ranking due to reliance on applied dose levels. We developed a parallelizable workflow to calculate benchmark dose (BMD) values from dynamic, high-content zebrafish behavior data that scales for high-throughput chemical screening. To capture the zebrafish movement response from light to dark stimulus, we summarized time-dependent data using both area under the curve and the immediate change at the transition point into two novel metrics that characterized abnormal behavior as a function of chemical concentration. The BMD workflow was applied to calculate BMD<sub>10</sub> values of 1,060 ToxCast chemicals for 24 zebrafish endpoints, including behavior, mortality and morphology. The BMD<sub>10</sub> values provided better precision and separation than LELs for clustering chemicals since they were derived from models that best-fit their concentration-response curves. Analysis of BMD<sub>10</sub> values revealed behavioral signatures as the most sensitive endpoints. High concordance in chemical activity was observed between ToxCast <i>in vitro</i> data and zebrafish <i>in vivo</i> behavioral data, however ToxPi analysis indicated that rankings based on <i>in vitro</i> data were not a reliable predictor of <i>in vivo</i> rankings for lower potency chemicals. This analysis method will enable the use of high-content zebrafish behavioral screening data for BMD analysis in toxicological hazard assessment.
