  In this paper, we report the results of our participation in the TREC-COVID
challenge. To meet the challenge of building a search engine for rapidly
evolving biomedical collection, we propose a simple yet effective weighted
hierarchical rank fusion approach, that ensembles together 102 runs from (a)
lexical and semantic retrieval systems, (b) pre-trained and fine-tuned BERT
rankers, and (c) relevance feedback runs. Our ablation studies demonstrate the
contributions of each of these systems to the overall ensemble. The submitted
ensemble runs achieved state-of-the-art performance in rounds 4 and 5 of the
TREC-COVID challenge.
