Indirect Inference (I‐I) estimation of structural parametersθrequires matching observed and simulated statistics, which are most often generated using an auxiliary model that depends on instrumental parametersβ. The estimators of the instrumental parameters will encapsulate the statistical information used for inference about the structural parameters. As such, artificially constraining these parameters may restrict the ability of the auxiliary model to accurately replicate features in the structural data, which may lead to a range of issues, such as a loss of identification. However, in certain situations the parametersβnaturally come with a set ofqrestrictions. Examples include settings whereβmust be estimated subject toqpossibly strict inequality constraintsg(β)>0, such as, when I‐I is based on GARCH auxiliary models. In these settings, we propose a novel I‐I approach that uses appropriately modified unconstrained auxiliary statistics, which are simple to compute and always exists. We state the relevant asymptotic theory for this I‐I approach without constraints and show that it can be reinterpreted as a standard implementation of I‐I through a properly modified binding function. Several examples that have featured in the literature illustrate our approach.