  Searching for black hole echo signals with gravitational waves provides a
means of probing the near-horizon regime of these objects. We demonstrate a
pipeline to efficiently search for these signals in gravitational wave data and
calculate model selection probabilities between signal and no-signal
hypotheses. As an example of its use we calculate Bayes factors for the
Abedi-Dykaar-Afshordi (ADA) model on events in LIGO's first observing run and
compare to existing results in the literature. We discuss the benefits of using
a full likelihood exploration over existing search methods that used template
banks and calculated p-values. We use the waveforms of ADA, although the method
is easily extendable to other waveforms. With these waveforms we are able to
demonstrate a range of echo amplitudes that is already is ruled out by the
data.
