  Establishing a good information retrieval system in popular mediums of
entertainment is a quickly growing area of investigation for companies and
researchers alike. We delve into the domain of information retrieval for
podcasts. In Spotify's Podcast Challenge, we are given a user's query with a
description to find the most relevant short segment from the given dataset
having all the podcasts. Previous techniques that include solely classical
Information Retrieval (IR) techniques, perform poorly when descriptive queries
are presented. On the other hand, models which exclusively rely on large neural
networks tend to perform better. The downside to this technique is that a
considerable amount of time and computing power are required to infer the
result. We experiment with two hybrid models which first filter out the best
podcasts based on user's query with a classical IR technique, and then perform
re-ranking on the shortlisted documents based on the detailed description using
a transformer-based model.
