We consider a bandit problem where at any time, the decision maker can add new arms to her consideration set. A new arm is queried at a cost from an arm-reservoir containing finitely many arm-types, each characterized by a distinct mean reward. The cost of query reflects in a diminishing probability of the returned arm being optimal, unbeknown to the decision maker; this feature encapsulates defining characteristics of a broad class of operations-inspired online learning problems, e.g., those arising in markets with churn, or those involving allocations subject to costly resource acquisition. The decision maker's goal is to maximize her cumulative expected payoffs over a sequence of n pulls, oblivious to the statistical properties as well as types of the queried arms. We study two natural modes of endogeneity in the reservoir distribution, and characterize a necessary condition for achievability of sub-linear regret in the problem. We also discuss a UCB-inspired adaptive algorithm that is long-run-average optimal whenever said condition is satisfied, thereby establishing its tightness.