  We report the automatic detection of 11 transients (7 possible supernovae and
4 active galactic nuclei candidates) within the Zwicky Transient Facility
fourth data release (ZTF DR4), all of them observed in 2018 and absent from
public catalogs. Among these, three were not part of the ZTF alert stream. Our
transient mining strategy employs 41 physically motivated features extracted
from both real light curves and four simulated light curve models (SN Ia, SN
II, TDE, SLSN-I). These features are input to a k-D tree algorithm, from which
we calculate the 15 nearest neighbors. After pre-processing and selection cuts,
our dataset contained approximately a million objects among which we visually
inspected the 105 closest neighbors from seven of our brightest, most
well-sampled simulations, comprising 89 unique ZTF DR4 sources. Our result
illustrates the potential of coherently incorporating domain knowledge and
automatic learning algorithms, which is one of the guiding principles directing
the SNAD team. It also demonstrates that the ZTF DR is a suitable testing
ground for data mining algorithms aiming to prepare for the next generation of
astronomical data.
