It would not be ‘endless’, since you would eventually run out of points to exclude!
I would stop with the first iteration. That identifies the most extreme outliers.
Actually, unless I had reason to believe that the outliers were due to some methodological problem in my data collection, which would certainly be a reason to examine them and possibly exclude them, I would keep all of them. This is an argument in favour of defining a protocol first, with exclusion criteria for data, such as sick animals, thermal noise in measuring equipment, equipment nonlinearities such as amplifier saturation or ‘railing’, and similar problems. Ideally, you will always have a valid reason for excluding some data, other than that they are simply ‘outliers’.
