Single-cell RNA-seq (scRNA-seq) has profiled hundreds of millions of human cells across organs, diseases, development, and perturbations to date. Mining these growing atlases could reveal cell-disease associations, discover cell states in unexpected tissue contexts, and relate in vivo biology to in vitro models. These require a common measure of cell similarity across the body and an efficient way to search. Here, we develop SCimilarity, a metric learning framework to learn a unified and interpretable representation that enables rapid queries of tens of millions of cell profiles from diverse studies for cells that are transcriptionally similar to an input cell profile or state. We use SCimilarity to query a 23.4 million cell atlas of 412 scRNA-seq studies for macrophage and fibroblast profiles from interstitial lung disease1 and reveal similar cell profiles across other fibrotic diseases and tissues. The top scoring in vitro hit for the macrophage query was a 3D hydrogel system2, which we experimentally demonstrated reproduces this cell state. SCimilarity serves as a foundation model for single-cell profiles that enables researchers to query for similar cellular states across the human body, providing a powerful tool for generating biological insights from the Human Cell Atlas.