In　the　ultra-high　dimensional　setting,　two　popular　variable　screening　methods　with　the　desirable　sure　screening　property　are　sure　independence　screening　(SIS)　and　forward　regression　(FR).　Both　are　classical　variable　screening　methods,　and　recently　have　attracted　greater　attention　under　high-dimensional　data　analysis.　We　consider　a　new　and　simple　screening　method　that　incorporates　multiple　predictors　at　each　step　of　forward　regression,　with　decisions　on　which　variables　to　incorporate　based　on　the　same　criterion.　If　only　one　step　is　carried　out,　the　new　procedure　reduces　to　SIS.　Thus　it　can　be　regarded　as　a　generalisation　and　unification　of　FR　and　SIS.　More　importantly,　it　preserves　the　sure　screening　property　and　has　computational　complexity　similar　to　FR　at　each　step,　yet　it　can　discover　the　relevant　covariates　in　fewer　steps.　Thus　it　reduces　the　computational　burden　of　FR　drastically　while　retaining　the　advantages　of　the　latter　over　SIS.　Furthermore,　we　show　that　it　can　find　all　the　true　variables　if　the　number　of　steps　taken　is　the　same　as　the　correct　model　size,　which　is　a　new　theoretical　result　even　for　the　original　FR.　An　extensive　simulation　study　and　application　to　two　real　data　examples　demonstrate　excellent　performance　of　the　proposed　method.