  Searching the data of gravitational-wave detectors for signals from compact
binary mergers is a computationally demanding task. Recently, machine learning
algorithms have been proposed to address current and future challenges.
However, the results of these publications often differ greatly due to
differing choices in the evaluation procedure. The Machine Learning
Gravitational-Wave Search Challenge was organized to resolve these issues and
produce a unified framework for machine-learning search evaluation. Six teams
submitted contributions, four of which are based on machine learning methods
and two are state-of-the-art production analyses. This paper describes the
submission from the team TPI FSU Jena and its updated variant. We also apply
our algorithm to real O3b data and recover the relevant events of the GWTC-3
catalog.
