  Efficient algorithms for searching for optimal saturated designs are widely
available. They maximize a given efficiency measure (such as D-optimality) and
provide an optimum design. Nevertheless, they do not guarantee a \emph{global}
optimal design. Indeed, they start from an initial random design and find a
local optimal design. If the initial design is changed the optimum found will,
in general, be different. A natural question arises. Should we stop at the
design found or should we run the algorithm again in search of a better design?
This paper uses very recent methods and software for discovery probability to
support the decision to continue or stop the sampling. A software tool written
in SAS has been developed.
