  To mitigate the model dependencies of searches for new narrow resonances at
the Large Hadron Collider (LHC), semi-supervised Neural Networks (NNs) can be
used. Unlike fully supervised classifiers these models introduce an additional
look-elsewhere effect in the process of optimising thresholds on the response
distribution. We perform a frequentist study to quantify this effect, in the
form of a trials factor. As an example, we consider simulated $Z\gamma$ data to
perform narrow resonance searches using semi-supervised NN classifiers. The
results from this analysis provide substantiation that the look-elsewhere
effect induced by the semi-supervised NN is under control.
