  We used data from the QUEST-La Silla Active Galactic Nuclei (AGN) variability
survey to construct light curves for 208,583 sources over $\sim 70$ deg$^2$,
with a a limiting magnitude $r \sim 21$. Each light curve has at least 40
epochs and a length of $\geq 200$ days. We implemented a Random Forest
algorithm to classify our objects as either AGN or non-AGN according to their
variability features and optical colors, excluding morphology cuts. We tested
three classifiers, one that only includes variability features (RF1), one that
includes variability features and also $r-i$ and $i-z$ colors (RF2), and one
that includes variability features and also $g-r$, $r-i$, and $i-z$ colors
(RF3). We obtained a sample of high probability candidates (hp-AGN) for each
classifier, with 5,941 candidates for RF1, 5,252 candidates for RF2, and 4,482
candidates for RF3. We divided each sample according to their $g-r$ colors,
defining blue ($g-r\leq 0.6$) and red sub-samples ($g-r>0.6$). We find that
most of the candidates known from the literature belong to the blue
sub-samples, which is not necessarily surprising given that, unlike for many
literature studies, we do not cut our sample to point-like objects. This means
that we can select AGN that have a significant contribution from redshifted
starlight in their host galaxies. In order to test the efficiency of our
technique we performed spectroscopic follow-up, confirming the AGN nature of 44
among 54 observed sources (81.5\% of efficiency). From the campaign we
concluded that RF2 provides the purest sample of AGN candidates.
