Incorporating safety data early in the drug discovery pipeline is key to reducing costly lead candidate failures. For a single drug development project, we estimate that several thousand samples per day require screening (<10 s per acquisition). While chromatography-based metabolomics has proven value at predicting toxicity from metabolic biomarker profiles, it lacks sufficiently high sample throughput. Acoustic mist ionization mass spectrometry (AMI-MS) is an atmospheric pressure ionization approach that can measure metabolites directly from 384-well plates with unparalleled speed. We sought to implement a signal processing and data analysis workflow to produce high-quality AMI-MS metabolomics data and to demonstrate its application to drug safety screening. An existing direct infusion mass spectrometry workflow was adapted, extended, optimized, and tested, utilizing three AMI-MS data sets acquired from technical and biological replicates of metabolite standards and HepG2 cell lysates and a toxicity study. Driven by criteria to minimize variance and maximize feature counts, an algorithm to extract the pulsed scan data was designed; parameters for signal-to-noise-ratio, replicate filter, sample filter, missing value filter, and RSD filter were all optimized; normalization and batch correction strategies were adapted; and cell phenotype filtering was implemented to exclude high cytotoxicity samples. The workflow was demonstrated using a highly replicated HepG2 toxicity data set, comprising 2772 samples from exposures to 16 drugs across 9 concentrations and generated in under 5 h, revealing metabolic phenotypes and individual metabolite changes that characterize specific modes of action. This AMI-MS workflow opens the door to ultrahigh-throughput metabolomics screening, increasing the rate of sample analysis by approximately 2 orders of magnitude.