In recent times, the growth of predictive cheminformatics signifies the inclination of researchers toward predictive modeling approaches, which not only provide reliability to the predictions but also eliminate ethical complications regarding animal procurement and experimentation. These approaches are simple, reproducible, fast, and economical, and thus have high acceptability to the research world. With the growth of predictive cheminformatics, newer approaches are constantly being developed to increase prediction accuracy. This can be exemplified in the case of the conventional QSAR modeling approach, which does not provide sufficient reliability to the prediction in case of a limited sample size. This has led to the adherence to non-statistical approaches like Read-Across, which provide reliability in predictions while dealing with limited sample size and thus act as a good data gap-filling tool. However, the lack of information on the quantitative contributions of the associated features is a major concern for Read-Across predictions. Therefore, to amalgamate the pros and eliminate the cons, recent research led to the merging of the Read-Across hypothesis to a statistical modeling framework, leading to the development of quantitative/classification Read-Across Structure-Activity Relationship (q-RASAR/c-RASAR) models. These models incorporate Read-Across into a statistical modeling framework that uses Read-Across-derived similarity and error measures as descriptors for QSAR. As evident from some of the case studies described in this chapter, the q-RASAR and c-RASAR models generate enhanced model statistics, especially the external predictivity, as compared to the corresponding QSAR models developed using the identical amount of chemical information. Moreover, from a statistical point-of-view, the q-RASAR and c-RASAR models mostly require a lower number of descriptors that enhance their statistical reliability. Within a short period, this approach gathered significant attention from the scientific community and warrants further exploration.