Deciphering cell−cell communication is a key step in understanding the physiology and pathology of multicellular systems. Recent advances in single-cell transcriptomics have contributed to unraveling the cellular composition of tissues and enabled the development of computational algorithms to predict cellular communication mediated by ligand−receptor interactions. Despite the existence of various tools capable of inferring cell−cell interactions from single-cell RNA sequencing data, the analysis and interpretation of the biological signals often require deep computational expertize. Here we present InterCellar, an interactive platform empowering lab-scientists to analyze and explore predicted cell−cell communication without requiring programming skills. InterCellar guides the biological interpretation through customized analysis steps, multiple visualization options, and the possibility to link biological pathways to ligand−receptor interactions. Alongside convenient data exploration features, InterCellar implements data-driven analyses including the possibility to compare cell−cell communication from multiple conditions. By analyzing COVID-19 and melanoma cell−cell interactions, we show that InterCellar resolves data-driven patterns of communication and highlights molecular signals through the integration of biological functions and pathways. We believe our user-friendly, interactive platform will help streamline the analysis of cell−cell communication and facilitate hypothesis generation in diverse biological systems.