We developed highly predictive classification models for human liver microsomal (HLM) stability using the apparent intrinsic clearance (CL(int, app)) as the end point. HLM stability has been shown to be an important factor related to the metabolic clearance of a compound. Robust in silico models that predict metabolic clearance are very useful in early drug discovery stages to optimize the compound structure and to select promising leads to avoid costly drug development failures in later stages. Using Random Forest and Bayesian classification methods with MOE, E-state descriptors, ADME Keys, and ECFP_6 fingerprints, various highly predictive models were developed. The best performance of the models shows 80 and 75% prediction accuracy for the test and validation sets, respectively. A detailed analysis of results will be shown, including an assessment of the prediction confidence, the significant descriptors, and the application of these models to drug discovery projects.