We introduce an algorithm that we call Practically Safe Extremum Seeking (PSfES), which seeks to minimize an unknown objective function while avoiding unsafe regions of state space, except for a possible minor violation of the safety boundaries, which can be arbitrarily reduced using the algorithm’s design parameters, such as the perturbation frequency and amplitude. We allow the metric of safety— a barrier function—to be functionally unknown. Only the value of the barrier function is assumed to be measured. We introduce dynamic filters which emulate, in an average and singularly perturbed sense, the feedback law of a standard quadratic programming (QP) and control barrier function (CBF) based safety filter, acting on a nominal extremum seeking (ES) controller. These filters have the effect of enabling the use of the averaging and singular perturbation theorems, which enable us to guarantee convergence (practical) to near a point in the safe set and safety (practical) during the transient, both in a local sense. We present a design for multiple dimensions but provide an analysis in one dimension. Finally, the behavior of our controller is demonstrated in simulation for two examples.