The COVID-19 pandemic has resulted in a rapidly growing quantity of scientific publications from journal articles, preprints, and other sources . The TREC-COVID Challenge was created to evaluate information retrieval (IR) methods and systems for this quickly expanding corpus . Using the COVID-19 Open Research Dataset (CORD-19), several dozen research teams participated in over 5 rounds of the TREC-COVID Challenge . While previous work has compared IR techniques used on other test collections, there are no studies that have analyzed the methods used by participants in the TREC-COVID Challenge . We manually reviewed team run reports from Rounds 2 and 5, extracted features from the documented methodologies, and used a univariate and multivariate regression-based analysis to identify features associated with higher retrieval performance . We observed that fine-tuning datasets with relevance judgments, MS-MARCO, and CORD-19 document vectors was associated with improved performance in Round 2 but not in Round 5 . Though the relatively decreased heterogeneity of runs in Round 5 may explain the lack of significance in that round, fine-tuning has been found to improve search performance in previous challenge evaluations by improving a system's ability to map relevant queries and phrases to documents . Furthermore, term expansion was associated with improvement in system performance, and the use of the narrative field in the TREC-COVID topics was associated with decreased system performance in both rounds . These findings emphasize the need for clear queries in search . While our study has some limitations in its generalizability and scope of techniques analyzed, we identified some IR techniques that may be useful in building search systems for COVID-19 using the TREC-COVID test collections.