  In simple open-domain question answering (QA), dense retrieval has become one
of the standard approaches for retrieving the relevant passages to infer an
answer. Recently, dense retrieval also achieved state-of-the-art results in
multi-hop QA, where aggregating information from multiple pieces of information
and reasoning over them is required. Despite their success, dense retrieval
methods are computationally intensive, requiring multiple GPUs to train. In
this work, we introduce a hybrid (lexical and dense) retrieval approach that is
highly competitive with the state-of-the-art dense retrieval models, while
requiring substantially less computational resources. Additionally, we provide
an in-depth evaluation of dense retrieval methods on limited computational
resource settings, something that is missing from the current literature.
