Quantitative structure-activity relationship (QSAR) modeling has matured over the past 50 years and has been very useful in discovering and optimizing drug leads. Although its roots were in extra-thermodynamic relationships within small sets of chemically similar molecules focused on mechanistic interpretation, a second class of QSAR models has emerged that relies on machine learning methods to generate models from large, chemically diverse data sets for predictive purposes. There has been a tension between the two groups of QSAR practitioners that is unnecessary and possibly counterproductive. This paper explains the difference in philosophy and application of these two distinct, but equally important, classes of QSAR models and how they can work together synergistically to accelerate the discovery of new drugs or materials.