  This paper reviews, analyzes, and proposes a new perspective on the
bi-encoder architecture for neural search. While the bi-encoder architecture is
widely used due to its simplicity and scalability at test time, it has some
notable issues such as low performance on seen datasets and weak zero-shot
performance on new datasets. In this paper, we analyze these issues and
summarize two main critiques: the encoding information bottleneck problem and
limitations of the basic assumption of embedding search. We then construct a
thought experiment to logically analyze the encoding and searching operations
and challenge the basic assumption of embedding search. Building on these
observations, we propose a new perspective on the bi-encoder architecture
called the \textit{encoding--searching separation} perspective, which
conceptually and practically separates the encoding and searching operations.
This new perspective is applied to explain the root cause of the identified
issues and discuss ways to mitigate the problems. Finally, we discuss the
implications of the ideas underlying the new perspective, the design surface
that it exposes and the potential research directions arising from it.
