  We consider the problem of finding a target object $t$ using pairwise
comparisons, by asking an oracle questions of the form \emph{"Which object from
the pair $(i,j)$ is more similar to $t$?"}. Objects live in a space of latent
features, from which the oracle generates noisy answers. First, we consider the
{\em non-blind} setting where these features are accessible. We propose a new
Bayesian comparison-based search algorithm with noisy answers; it has low
computational complexity yet is efficient in the number of queries. We provide
theoretical guarantees, deriving the form of the optimal query and proving
almost sure convergence to the target $t$. Second, we consider the \emph{blind}
setting, where the object features are hidden from the search algorithm. In
this setting, we combine our search method and a new distributional triplet
embedding algorithm into one scalable learning framework called
\textsc{Learn2Search}. We show that the query complexity of our approach on two
real-world datasets is on par with the non-blind setting, which is not
achievable using any of the current state-of-the-art embedding methods.
Finally, we demonstrate the efficacy of our framework by conducting an
experiment with users searching for movie actors.
