Protein-protein interactions underpin nearly all cellular processes, making their accurate identification a central challenge in biology. With the rapid expansion of genomic data, sequence-based computational approaches have emerged as a powerful route to infer such interactions, complementing experimental methods that are often prohibitively time- and resource-intensive. This challenge becomes particularly acute in the presence of paralogs, which arise through gene duplication and typically diversify toward distinct, though sometimes overlapping, functions. Reconstructing their interaction networks is therefore essential for understanding a wide range of biological processes. Protein paralogs within a family can often be subdivided into classes based on a range of properties, including functional, structural and architectural features. When interactions between these classes are conserved across organisms, such that sequences from one class interact exclusively with sequences from another, this information can be used to solve the paralog matching problem. We introduce here CLAPP (CLAss Pooling for Paralog matching), a method for predicting interacting paralogs by pooling interaction scores from different subclasses across organisms. We apply it to scores extracted using coevolution-based methods. Pooling scores at the class level reduces noise in the interaction scores and replaces organism-specific assignments with a single shared assignment, improving performance and substantially reducing computational cost. We apply CLAPP to bacterial systems including histidine kinases and response regulators, as well as interacting families of chaperones and co-chaperones, and recover known interaction partners.