The ALMA Frontier Fields Survey III: 1.1 mm Emission Line
  Identifications in Abell 2744, MACSJ0416.1-2403, MACSJ1149.5+2223, Abell 370,
  and Abell S1063
  Most sub-mm emission line studies of galaxies to date have targeted sources
with known redshifts where the frequencies of the lines are well constrained.
Recent blind line scans circumvent the spectroscopic redshift requirement,
which could represent a selection bias.
  Our aim is to detect emission lines present in continuum oriented
observations. The detection of such lines provides spectroscopic redshift and
yields properties of the galaxies.
  We perform a search for emission lines in the ALMA observations of five
Frontier Fields clusters and assess the reliability of our detection by
associating line candidates with detected galaxies in deep near-infrared
imaging.
  We find 26 significant emission lines candidates, with observed line fluxes
between 0.2-4.6 Jy km s$^{-1}$ and velocity dispersions (FWHM) of 25-600 km
s$^{-1}$. Nine of these candidates lie nearby to near-infrared sources,
boosting their reliability; in six cases the observed line frequency and
strength are consistent with expectations given the photometric redshift and
properties of the galaxy counterparts. We present redshift identifications,
magnifications and molecular gas estimates for the galaxies with identified
lines. We show that two of these candidates likely originate from starburst
galaxies, one of which is a jellyfish galaxy, while another two are consistent
with being main sequence galaxies based in their depletion times.
  This work highlights the degree to which serendipitous emission lines can be
discovered in large mosaic continuum observations when deep ancillary data are
available. The low number of high-significance line detections, however,
confirms that such surveys are not as optimal as blind line scans. We stress
that Monte Carlo simulations should be used to assess the line detections
significances, since using the negative noise suffers from stochasticity and
incurs larger uncertainties.
