  (Abridged) Space missions to search for exo-planets via the transit method,
such as COROT, Eddington and Kepler, will need to address problems associated
with the automated and efficient detection of planetary transits in light
curves affected by a variety of noise sources, including stellar variabilility.
Starting from a general purpose maximum likelihood approach we discuss the
links between a variety of period and transit finding methods. The natural
endpoint of this hierarchy of methods is shown to be a fast, robust and
statistically efficient least-squares algorithm based on box-shaped transits.
  This approach is predicated on the assumption of periodic transits hidden in
random noise, usually assumed to be superposed on a flat continuum with regular
continuous sampling. We next show how to generalise the transit finding method
to the more realistic scenario where complex stellar (micro) variability,
irregular sampling and long gaps in the data are all present. Tests of this
methodology on simulated Eddington light curves including realistic stellar
micro-variability, irregular sampling and data gaps, are used to quantify the
performance. In the case where transit durations are short compared to the
dominant timescales for stellar variability and data record segments, it is
possible to decouple the transit signal from the remainder.
  We conclude that even with realistic contamination from stellar variability,
irregular sampling, and gaps in the data record, it is still possible to detect
transiting planets with an efficiency close to the idealised theoretical bound.
In particular, space missions have the potential to approach the regime of
detecting earth-like planets around G2V-type stars.
