  With the upcoming plethora of astronomical time-domain datasets and surveys,
anomaly detection as a way to discover new types of variable stars and
transients has inspired a new wave of research. Yet, the fundamental definition
of what constitutes an anomaly and how this depends on the overall properties
of the population of light curves studied remains a discussed issue. Building
on a previous study focused on Kepler light curves, we present an analysis that
uses the Unsupervised Random Forest to search for anomalies in TESS light
curves. We provide a catalogue of anomalous light curves, classify them
according to their variability characteristics and associate their anomalous
nature to any particular evolutionary stage or astrophysical configuration. For
anomalies belonging to known classes (e.g. eclipsing binaries), we have
investigated which physical parameters drive the anomaly score. We find a
combination of unclassified anomalies and objects of a known class with
outlying physical configurations, such as rapid pulsators, deep eclipsing
binaries of long periods, and irregular light curves due to obscuration in
YSOs. Remarkably, we find that the set of anomalous types differ between the
Kepler and TESS datasets, indicating that the overall properties of the parent
population are an important driver of anomalous behaviour.
