Throughout the past decade, the expectations from the regulatory agencies for safety, drug-drug interactions (DDIs), pharmacokinetic, and disposition characterization of new chemical entities (NCEs) by pharmaceutical companies seeking registration have increased. DDIs are frequently assessed using in silico, in vitro, and in vivo methodologies. However, a key gap in this screening paradigm is a full structural understanding of time-dependent inhibition (TDI) on the cytochrome P450 systems, particularly P450 3A4. To address this, a number of high-throughput in vitro assays have been developed. This work describes an automated assay for TDI using two concentrations at two time points (2 + 2 assay). Data generated with this assay for over 2000 compounds from multiple therapeutic programs were used to generate in silico Bayesian classification models of P450 3A4-mediated TDI. These in silico models were validated using several external test sets and multiple random group testing (receiver operator curve value >0.847). We identified a number of substructures that were likely to elicit TDI, the majority containing indazole rings. These in vitro and in silico approaches have been implemented as a part of the Pfizer screening paradigm. The Bayesian models are available on the intranet to guide synthetic strategy, predict whether a NCE is likely to cause a TDI via P450 3A4, filter for in vitro testing, and identify substructures important for TDI as well as those that do not cause TDI. This represents an integrated in silico-in vitro strategy for addressing P450 3A4 TDI and improving the efficiency of screening.