We present an overview of the TREC-COVID Challenge, an information retrieval (IR) shared task to evaluate search on scientific literature related to COVID-19 . The goals of TREC-COVID include the construction of a pandemic search test collection and the evaluation of IR methods for COVID-19 . The challenge was conducted over five rounds from April to July , 2020, with participation from 92 unique teams and 556 individual submissions . A total of 50 topics (sets of related queries) were used in the evaluation, starting at 30 topics for Round 1 and adding 5 new topics per round to target emerging topics at that state of the still-emerging pandemic . This paper provides a comprehensive overview of the structure and results of TREC-COVID . Specifically, the paper provides details on the background, task structure, topic structure, corpus, participation, pooling, assessment, judgments, results, top-performing systems, lessons learned, and benchmark datasets.