  We present an unsupervised search for outliers in the Bright Galaxy Survey
(BGS) dataset from the DESI Early Data Release. This analysis utilizes an
autoencoder to compress galaxy spectra into a compact, redshift-invariant
latent space, and a normalizing flow to identify low-probability objects. The
most prominent outliers show distinctive spectral features such as irregular or
double-peaked emission lines, or originate from galaxy mergers, blended
sources, and rare quasar types, including one previously unknown Broad
Absorption Line system. A significant portion of the BGS outliers are stars
spectroscopically misclassified as galaxies. By building our own star model
trained on spectra from the DESI Milky Way Survey, we have determined that the
misclassification likely stems from the Principle Component Analysis of stars
in the DESI pipeline. To aid follow-up studies, we make the full probability
catalog of all BGS objects and our pre-trained models publicly available.
