  Information overload is a prevalent challenge in many high-value domains. A
prominent case in point is the explosion of the biomedical literature on
COVID-19, which swelled to hundreds of thousands of papers in a matter of
months. In general, biomedical literature expands by two papers every minute,
totalling over a million new papers every year. Search in the biomedical realm,
and many other vertical domains is challenging due to the scarcity of direct
supervision from click logs. Self-supervised learning has emerged as a
promising direction to overcome the annotation bottleneck. We propose a general
approach for vertical search based on domain-specific pretraining and present a
case study for the biomedical domain. Despite being substantially simpler and
not using any relevance labels for training or development, our method performs
comparably or better than the best systems in the official TREC-COVID
evaluation, a COVID-related biomedical search competition. Using distributed
computing in modern cloud infrastructure, our system can scale to tens of
millions of articles on PubMed and has been deployed as Microsoft Biomedical
Search, a new search experience for biomedical literature:
https://aka.ms/biomedsearch.
