Feature　screening　in　ultrahigh-dimensional　varying　coefficient　models　is　a　crucial　statistical　problem　in　economics,　genomics,　etc.　Current　methods　not　only　suffer　from　circumstances　when　the　models　involve　multiple　index　variables　or　group　predictor　variables,　but　also　cannot　handle　nonlinear　varying　coefficient　models.　To　address　these　real　life　scenarios　efficiently,　we　develop　a　screening　procedure　for　ultrahigh-dimensional　varying　coefficient　models　utilizing　conditional　distance　covariance　(CDC).　Extensive　simulation　studies　and　two　real　economic　data　examples　show　the　effectiveness　and　the　flexibility　of　our　proposed　method.