The screening of chemical libraries with traditional methods, such as high-throughput screening (HTS), is expensive and time consuming. Quantitative structure–activity relation (QSAR) modeling is an alternative method that can assist in the selection of lead molecules by using the information from reference active and inactive compounds. This approach requires good molecular descriptors that are representative of the molecular features responsible for the relevant molecular activity. The usefulness of these descriptors in QSAR studies has been extensively demonstrated, and they have also been used as a measure of structural similarity or diversity. In this review, we provide a brief explanation of descriptors and the selection approaches most commonly used in QSAR experiments. In addition, some studies have also demonstrated the positive influence of features selection for any drug development model.