  With impressive results in applications relying on feature learning, deep
learning has also blurred the line between algorithm and data. Pick a training
dataset, pick a backbone network for feature extraction, and voil\`a ; this
usually works for a variety of use cases. But the underlying hypothesis that
there exists a training dataset matching the use case is not always met.
Moreover, the demand for interconnections regardless of the variations of the
content calls for increasing generalization and robustness in features.
  An interesting application characterized by these problematics is the
connection of historical and cultural databases of images. Through the
seemingly simple task of instance retrieval, we propose to show that it is not
trivial to pick features responding well to a panel of variations and semantic
content. Introducing a new enhanced version of the Alegoria benchmark, we
compare descriptors using the detailed annotations. We further give insights
about the core problems in instance retrieval, testing four state-of-the-art
additional techniques to increase performance.
