SciRadar is a semantic search engine designed to locate Digital Object Identifiers (DOIs) and publication details for scientific papers using natural language queries. Unlike traditional academic search engines that rely on exact keyword matching and citation frequency, SciRadar compares the underlying meaning of a user’s query against the content of indexed studies. It uses language model embeddings to rank results by conceptual relevance rather than by exact wording or citation count. This approach helps surface related research even when it uses different terminology than the query. The tool is free to use and does not require an account, sign up, or display advertising. Its bibliographic metadata and abstracts are sourced from the U.S. National Library of Medicine (NLM) via PubMed/MEDLINE. SciRadar is an independent project and is not affiliated with, endorsed by, or sponsored by the NLM or NCBI. Abstracts remain the copyright of their respective publishers. The index covers a growing subset of the scientific literature and is expanded continuously, though it is not exhaustive and some studies may not yet be included. Users interact with SciRadar by typing a research question as a natural sentence, in plain language, rather than as a list of isolated keywords. The system converts the description into a vector embedding and compares it against scientific studies based on semantic similarity. Results are ranked by relevance to the query meaning, not by popularity. From the results list, users can view publication details or jump directly to the publisher through the paper’s DOI. Typical use cases include exploratory literature searches for topics where the user is not certain of the standard terminology, as well as finding conceptually related work that might be overlooked by keyword-based systems.