Determining the interacting proteins in multiprotein complexes can be technically challenging. An emerging biochemical approach to this end is based on the ‘thermal proximity co-aggregation’ (TPCA) phenomenon. Accordingly, when two or more proteins interact to form a complex, they tend to co-aggregate when subjected to heat-induced denaturation and thus exhibit similar melting curves. Here, we explore the potential of leveraging TPCA for determining protein interactions. We demonstrate that dissimilarity measure-based information retrieval applied to melting curves tends to rank a protein-of-interest’s interactors higher than its non-interactors, as shown in the context of pull-down assay results. Consequently, such rankings can reduce the number of confirmatory biochemical experiments needed to find bona fide protein–protein interactions. In general, rankings based on dissimilarity measures generated through metric learning further reduce the required number of experiments compared to those based on standard dissimilarity measures such as Euclidean distance. When a protein mixture’s melting curves are obtained in two conditions, we propose a scoring function that uses melting curve data to inform how likely a protein pair is to interact in one condition but not another. We show that ranking protein pairs by their scores is an effective approach for determining condition-specific protein–protein interactions. By contrast, clustering melting curve data generally does not inform about the interacting proteins in multiprotein complexes. In conclusion, we report improved methods for dissimilarity measure-based computation of melting curves data that can greatly enhance the determination of interacting proteins in multiprotein complexes.