An optimum approach to discover new, non-toxic organic corrosion inhibitors would be to identify or design candidate inhibitor species as a function of substrate and environment using a reliable computational model followed by experimental verification. To date, quantitative models have been limited in accuracy and chemical breadth, and supportive corrosion data have relied on time consuming weight loss or one-by-one electrochemical experiments. This investigation has developed a rapid, robust, and predictive computational model that uses a molecule as a simplified substrate proxy and a combination of quantitative descriptors for the inhibitor-substrate bonding energy and the inhibitor molecule hydrophobicity. The predicted inhibitor efficiency of a diverse series of inhibitors is compared to experimental results acquired using electrochemical impedance spectroscopy on A36 steel in 0.1 M HCl and the following organic compounds: imidazole, 2-methylimidazole, benzimidazole, piperazine, histidine, and an imidazoline-surfactant with the commercial name COZ. It was found that the combination of calculated bonding energy and hydrophobicity provide a high correlation between the computational model and experimentally measured corrosion inhibition efficiency of the various molecules. (C) 2014 The Electrochemical Society. All rights reserved.